import math
import os

import torch
import torch.optim as optim
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from skimage import io
from timm.layers import DropPath, to_2tuple, trunc_normal_
from torch import nn
from torch.nn import init
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torchvision import transforms


class CustomDataset(Dataset):
    def __init__(self, input_dir, mask_dir, transform=None):
        self.input_dir = input_dir
        self.mask_dir = mask_dir
        self.input_name = os.listdir(input_dir)
        self.transform = transform

    def __len__(self):
        return len(self.input_name)

    def __getitem__(self, idx):
        img_path = os.path.join(self.input_dir, self.input_name[idx])
        mask_path = os.path.join(self.mask_dir, self.input_name[idx])

        image = io.imread(img_path)
        mask = io.imread(mask_path, as_gray=True)
        mask = mask.squeeze(0)  # 先去掉大小为1的维度
        mask = mask.reshape(image.shape[0], image.shape[1], 1)  # 在最后一个维度添加一个大小为1的维度

        # 调整图像大小
        image = transforms.Resize((224, 224))(transforms.ToTensor()(image))
        mask = transforms.Resize((224, 224))(transforms.ToTensor()(mask))

        # 应用数据转换
        if self.transform:
            image = self.transform(image)

        return image, mask


class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] //
                              patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim,
                              kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * \
                (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        else:
            shifted_x = x

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)

        down = None
        if self.downsample is not None:
            down = self.downsample(x)
        return x, down

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops


class SwinEncoder(nn.Module):
    def __init__(self, img_size=224, patch_size=4, in_chans=3,
                 high_level_idx=None, low_level_idx=None, low_level_after_block=False, high_level_after_block=True,
                 embed_dim=96, depths=[2, 2, 6], num_heads=[3, 6, 12],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, high_level_norm=False, low_level_norm=False, ape=False, patch_norm=True,
                 use_checkpoint=False, **kwargs):

        super().__init__()

        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.mlp_ratio = mlp_ratio

        self.high_level_idx = high_level_idx
        self.low_level_idx = low_level_idx
        self.low_level_after_block = low_level_after_block
        self.high_level_after_block = high_level_after_block

        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)

        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(
                torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate,
                                                sum(depths))]  # stochastic depth decay rule

        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                               use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        # storing sizes and dimensions of the outputs
        self.high_level_size = img_size // (4 * 2 ** high_level_idx)
        self.high_level_dim = int(embed_dim * 2 ** high_level_idx)
        self.low_level_dim = int(embed_dim * 2 ** low_level_idx)

        self.high_level_norm = norm_layer(self.high_level_dim) if high_level_norm else None
        self.low_level_norm = norm_layer(self.low_level_dim) if low_level_norm else None

    def forward(self, x):
        """
        x: input batch with shape (batch_size, in_chans, img_size, img_size)

        returns
            1. low_level_features with shape (batch_size, low_size, low_size, low_chans)
            2. high_level_features with shape (batch_size, high_size, high_size, high_chans)
        """
        if x.size()[1] == 1:
            x = x.repeat(1, 3, 1, 1)

        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)

        low_level = high_level = None
        if self.low_level_idx == 0 and not self.low_level_after_block:
            low_level = x

        down = None
        depth = 0

        for layer in self.layers:
            x, down = layer(x if down is None else down)

            if depth == self.low_level_idx and self.low_level_after_block:
                low_level = x
            if depth == self.high_level_idx and self.high_level_after_block:
                high_level = x

            depth += 1

            if depth == self.low_level_idx and not self.low_level_after_block:
                low_level = down
            if depth == self.high_level_idx and not self.high_level_after_block:
                high_level = down

        if self.high_level_norm is not None:
            high_level = self.high_level_norm(high_level)
        if self.low_level_norm is not None:
            low_level = self.low_level_norm(low_level)

        low_size = int(math.sqrt(low_level.size(1)))
        high_size = int(math.sqrt(high_level.size(1)))

        low_level = low_level.view(-1, low_size, low_size, low_level.shape[-1])
        high_level = high_level.view(-1, high_size, high_size, high_level.shape[-1])

        return low_level, high_level


class SwinASPP(nn.Module):
    def __init__(self, input_size, input_dim, out_dim, cross_attn,
                 depth, num_heads, mlp_ratio, qkv_bias, qk_scale,
                 drop_rate, attn_drop_rate, drop_path_rate,
                 norm_layer, aspp_norm, aspp_activation, start_window_size,
                 aspp_dropout, downsample, use_checkpoint):

        super().__init__()

        self.out_dim = out_dim
        if input_size == 24:
            self.possible_window_sizes = [4, 6, 8, 12, 24]
        else:
            self.possible_window_sizes = [i for i in range(start_window_size, input_size + 1) if input_size % i == 0]

        self.layers = nn.ModuleList()
        for ws in self.possible_window_sizes:
            layer = BasicLayer(dim=int(input_dim),
                               input_resolution=(input_size, input_size),
                               depth=1 if ws == input_size else depth,
                               num_heads=num_heads,
                               window_size=ws,
                               mlp_ratio=mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=drop_path_rate,
                               norm_layer=norm_layer,
                               downsample=downsample,
                               use_checkpoint=use_checkpoint)

            self.layers.append(layer)

        if cross_attn == 'CBAM':
            self.proj = CBAMBlock(input_dim=len(self.layers) * input_dim,
                                  reduction=12,
                                  input_size=input_size,
                                  out_dim=out_dim)
        else:
            self.proj = nn.Linear(len(self.layers) * input_dim, out_dim)

        # Check if needed
        self.norm = norm_layer(out_dim) if aspp_norm else None
        if aspp_activation == 'relu':
            self.activation = nn.ReLU()
        elif aspp_activation == 'gelu':
            self.activation = nn.GELU()
        elif aspp_activation is None:
            self.activation = None

        self.dropout = nn.Dropout(aspp_dropout)

    def forward(self, x):
        """
        x: input tensor (high level features) with shape (batch_size, input_size, input_size, input_dim)

        returns ...
        """
        B, H, W, C = x.shape
        x = x.view(B, H * W, C)

        features = []
        for layer in self.layers:
            out, _ = layer(x)
            features.append(out)

        features = torch.cat(features, dim=-1)
        features = self.proj(features)

        # Check if needed
        if self.norm is not None:
            features = self.norm(features)
        if self.activation is not None:
            features = self.activation(features)
        features = self.dropout(features)

        return features.view(B, H, W, self.out_dim)


class ChannelAttention(nn.Module):
    def __init__(self, channel, reduction):
        super().__init__()
        self.maxpool = nn.AdaptiveMaxPool2d(1)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.se = nn.Sequential(
            nn.Conv2d(channel, channel // reduction, 1, bias=False),
            nn.ReLU(),
            nn.Conv2d(channel // reduction, channel, 1, bias=False)
        )
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        max_result = self.maxpool(x)
        avg_result = self.avgpool(x)
        max_out = self.se(max_result)
        avg_out = self.se(avg_result)
        output = self.sigmoid(max_out + avg_out)
        return output


class SpatialAttention(nn.Module):
    def __init__(self, kernel_size):
        super().__init__()
        self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=kernel_size // 2)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        max_result, _ = torch.max(x, dim=1, keepdim=True)
        avg_result = torch.mean(x, dim=1, keepdim=True)
        result = torch.cat([max_result, avg_result], 1)
        output = self.conv(result)
        output = self.sigmoid(output)
        return output


class CBAMBlock(nn.Module):

    def __init__(self, input_dim, reduction, input_size, out_dim):
        super().__init__()
        self.input_size = input_size
        self.ca = ChannelAttention(channel=input_dim, reduction=reduction)
        self.sa = SpatialAttention(kernel_size=1)

        self.proj = nn.Linear(input_dim, out_dim)

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        B, L, C = x.shape
        assert L == self.input_size ** 2
        x = x.permute(0, 2, 1).contiguous()
        x = x.view(B, C, self.input_size, self.input_size)

        residual = x
        out = x * self.ca(x)
        out = out * self.sa(out)
        out = out + residual
        out = out.view(B, C, L).permute(0, 2, 1).contiguous()
        return self.proj(out)


class PatchExpand(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=2, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.expand = nn.Linear(dim, 4 * dim, bias=False) if dim_scale == 2 else nn.Identity()
        self.norm = norm_layer(dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        x = self.expand(x)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=2, p2=2, c=C // 4)
        x = x.view(B, -1, C // 4)
        x = self.norm(x)

        return x


class FinalPatchExpand_X4(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=4, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.dim_scale = dim_scale
        self.expand = nn.Linear(dim, 16 * dim, bias=False)
        self.output_dim = dim
        self.norm = norm_layer(self.output_dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        x = self.expand(x)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
                      c=C // (self.dim_scale ** 2))
        x = x.view(B, -1, self.output_dim)
        x = self.norm(x)

        return x


class BasicLayer_up(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, upsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if upsample is not None:
            self.upsample = PatchExpand(input_resolution, dim=dim, dim_scale=2, norm_layer=norm_layer)
        else:
            self.upsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.upsample is not None:
            x = self.upsample(x)
        return x


class SwinDecoder(nn.Module):
    def __init__(self, low_level_idx, high_level_idx,
                 input_size, input_dim, num_classes,
                 depth, last_layer_depth, num_heads, window_size, mlp_ratio, qkv_bias, qk_scale,
                 drop_rate, attn_drop_rate, drop_path_rate, norm_layer, decoder_norm, use_checkpoint):
        super().__init__()
        self.low_level_idx = low_level_idx
        self.high_level_idx = high_level_idx

        self.layers_up = nn.ModuleList()
        for i in range(high_level_idx - low_level_idx):
            layer_up = BasicLayer_up(dim=int(input_dim),
                                     input_resolution=(input_size * 2 ** i, input_size * 2 ** i),
                                     depth=depth,
                                     num_heads=num_heads,
                                     window_size=window_size,
                                     mlp_ratio=mlp_ratio,
                                     qkv_bias=qkv_bias, qk_scale=qk_scale,
                                     drop=drop_rate, attn_drop=attn_drop_rate,
                                     drop_path=drop_path_rate,
                                     norm_layer=norm_layer,
                                     upsample=PatchExpand,
                                     use_checkpoint=use_checkpoint)

            self.layers_up.append(layer_up)

        self.last_layers_up = nn.ModuleList()
        for _ in range(low_level_idx + 1):
            i += 1
            last_layer_up = BasicLayer_up(dim=int(input_dim) * 2,
                                          input_resolution=(input_size * 2 ** i, input_size * 2 ** i),
                                          depth=last_layer_depth,
                                          num_heads=num_heads,
                                          window_size=window_size,
                                          mlp_ratio=mlp_ratio,
                                          qkv_bias=qkv_bias, qk_scale=qk_scale,
                                          drop=drop_rate, attn_drop=attn_drop_rate,
                                          drop_path=0.0,
                                          norm_layer=norm_layer,
                                          upsample=PatchExpand,
                                          use_checkpoint=use_checkpoint)
            self.last_layers_up.append(last_layer_up)

        i += 1
        self.final_up = PatchExpand(input_resolution=(input_size * 2 ** i, input_size * 2 ** i),
                                    dim=int(input_dim) * 2,
                                    dim_scale=2,
                                    norm_layer=norm_layer)

        if decoder_norm:
            self.norm_up = norm_layer(int(input_dim) * 2)
        else:
            self.norm_up = None
        self.output = nn.Conv2d(int(input_dim) * 2, num_classes, kernel_size=1, bias=False)

    def forward(self, low_level, aspp):
        """
        low_level: B, Hl, Wl, C
        aspp: B, Ha, Wa, C
        """
        B, Hl, Wl, C = low_level.shape
        _, Ha, Wa, _ = aspp.shape

        low_level = low_level.view(B, Hl * Wl, C)
        aspp = aspp.view(B, Ha * Wa, C)

        for layer in self.layers_up:  # 示例图里没这么做啊
            aspp = layer(aspp)

        x = torch.cat([low_level, aspp], dim=-1)

        for layer in self.last_layers_up:
            x = layer(x)

        if self.norm_up is not None:
            x = self.norm_up(x)

        x = self.final_up(x)

        B, L, C = x.shape
        H = W = int(math.sqrt(L))
        x = x.view(B, H, W, C)
        x = x.permute(0, 3, 1, 2).contiguous()
        x = self.output(x)

        return x


class SwinDeepLab(nn.Module):
    def __init__(self, num_class=1):
        super().__init__()
        # 初始化 SwinEncoder 使用默认参数
        self.encoder = SwinEncoder(high_level_idx=2, low_level_idx=0)
        self.num_class = num_class
        # 初始化 SwinDecoder 和 SwinASPP
        self.decoder = SwinDecoder(
            low_level_idx=0,  # 示例值，需要根据实际网络结构指定
            high_level_idx=2,  # 示例值，需要根据实际网络结构指定
            input_size=14,
            input_dim=96,
            num_classes=self.num_class,  # 假设有num_class个分类
            depth=3,  # 示例值，需要根据实际情况调整
            last_layer_depth=6,
            num_heads=3,
            window_size=7,
            mlp_ratio=4.0,
            qkv_bias=True,
            qk_scale=None,
            drop_rate=0.0,
            attn_drop_rate=0.0,
            drop_path_rate=0.1,
            norm_layer=nn.LayerNorm,
            decoder_norm=True,
            use_checkpoint=False,
        )

        self.aspp = SwinASPP(
            input_size=14,
            input_dim=384,
            out_dim=96,
            cross_attn='CBAM',
            depth=2,
            num_heads=3,
            mlp_ratio=4.0,  # 推荐：MLP 的隐藏层是输入层的四倍
            qkv_bias=True,  # 推荐：启用 QKV 计算的偏置项
            qk_scale=None,  # 推荐：让模型自动计算 QK 缩放
            drop_rate=0.1,  # 推荐：常用的 Dropout 率
            attn_drop_rate=0.1,  # 推荐：注意力层的 Dropout 率
            drop_path_rate=0.1,  # 推荐：路径丢弃率
            norm_layer=nn.LayerNorm,  # 推荐：使用 LayerNorm
            aspp_norm=True,  # 假设：在 ASPP 输出后应用正则化
            aspp_activation='relu',  # 推荐：ReLU 激活函数，也可以考虑使用 GELU
            start_window_size=7,  # 推荐：开始窗口大小与 input_size 相等
            aspp_dropout=0.1,  # 推荐：ASPP模块的 Dropout 率
            downsample=None,  # 假设：不在 ASPP 中进一步下采样
            use_checkpoint=False  # 假设：不使用梯度检查点以节省内存

        )

    def run_encoder(self, x):
        low_level, high_level = self.encoder(x)
        return low_level, high_level

    def run_aspp(self, x):
        return self.aspp(x)

    def run_decoder(self, low_level, pyramid):
        return self.decoder(low_level, pyramid)

    def forward(self, x):
        low_level, high_level = self.run_encoder(x)
        x = self.run_aspp(high_level)
        x = self.run_decoder(low_level, x)

        return nn.Sigmoid()(x)


def train(model, dataloader, criterion, optimizer, device, num_epochs=31):
    model.train()
    for epoch in range(num_epochs):
        print(f'第{epoch + 1}轮训练:')
        for images, masks in dataloader:
            images = images.float().to(device)
            masks = masks.float().to(device)

            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, masks)
            loss.backward()
            optimizer.step()

        print(f'Epoch {epoch}, Loss: {loss.item()}')
        if (epoch + 1) % 3 == 0:
            torch.save(model.state_dict(), f'../pt_file/Unet_ConvTranspose2d_better-{epoch}.pt')


if __name__ == '__main__':
    # 数据集与数据加载器
    # 可以根据需求进行数据增强操作
    transform = transforms.Compose([
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    input_dir = '../unet_img/imgs/val'
    mask_dir = '../unet_img/masks/val'
    dataset = CustomDataset(input_dir, mask_dir, transform=transform)
    dataloader = DataLoader(dataset, batch_size=8, shuffle=True)

    # 初始化模型、损失函数和优化器
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = SwinDeepLab(1).to(device)
    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # 开始训练
    train(model, dataloader, criterion, optimizer, device)
